"""
Modified from https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
"""
import oneflow as flow
import oneflow.nn as nn
from flowvision.layers import trunc_normal_
from .registry import ModelCreator
from .utils import load_state_dict_from_url
# Note that model with `in22k` means pretrained weight on imagenet22k dataset
model_urls = {
"swin_tiny_patch4_window7_224": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Swin_Transformer/swin_tiny_patch4_window7_224.zip",
"swin_small_patch4_window7_224": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Swin_Transformer/swin_small_patch4_window7_224.zip",
"swin_base_patch4_window7_224": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Swin_Transformer/swin_base_patch4_window7_224.zip",
"swin_base_patch4_window12_384": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Swin_Transformer/swin_base_patch4_window12_384.zip",
"swin_base_patch4_window7_224_in22k_to_1k": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Swin_Transformer/swin_base_patch4_window7_224_in22k_to_1k.zip",
"swin_base_patch4_window12_384_in22k_to_1k": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Swin_Transformer/swin_base_patch4_window12_384_in22k_to_1k.zip",
"swin_large_patch4_window7_224_in22k_to_1k": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Swin_Transformer/swin_large_patch4_window7_224_in22k_to_1k.zip",
"swin_large_patch4_window12_384_in22k_to_1k": "https://oneflow-public.oss-cn-beijing.aliyuncs.com/model_zoo/flowvision/classification/Swin_Transformer/swin_large_patch4_window12_384_in22k_to_1k.zip",
}
# helpers
def to_2tuple(x):
return (x, x)
def drop_path(x, drop_prob: float = 0.5, training: bool = False):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
'survival rate' as the argument.
"""
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (
x.ndim - 1
) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + flow.rand(*shape, dtype=x.dtype, device=x.device)
random_tensor = random_tensor.floor() # binarize
output = x.div(keep_prob) * random_tensor
return output
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x)
def window_partition(x, window_size):
B, H, W, C = x.shape
x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
windows = (
x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
)
return windows
def window_reverse(windows, window_size, H, W):
B = int(windows.shape[0] / (H * W / window_size / window_size))
x = windows.view(
B, H // window_size, W // window_size, window_size, window_size, -1
)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
return x
class Mlp(nn.Module):
def __init__(
self,
in_features,
hidden_features=None,
out_features=None,
act_layer=nn.GELU,
drop=0.0,
):
super().__init__()
out_features = out_features or in_features
hidden_features = hidden_features or in_features
self.fc1 = nn.Linear(in_features, hidden_features)
self.act = act_layer()
self.fc2 = nn.Linear(hidden_features, out_features)
self.drop = nn.Dropout(drop)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.drop(x)
x = self.fc2(x)
x = self.drop(x)
return x
class WindowAttention(nn.Module):
r""" Window based multi-head self attention (W-MSA) module with relative position bias.
It supports both of shifted and non-shifted window.
Args:
dim (int): Number of input channels
window_size (tuple[int]): The height and width of the window
num_heads (int): Number of attention heads
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: ``True``
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional): Dropout ratio of attention weight. Default: ``0.0``
proj_drop (float, optional): Dropout ratio of output. Default: ``0.0``
"""
def __init__(
self,
dim,
window_size,
num_heads,
qkv_bias=True,
qk_scale=None,
attn_drop=0.0,
proj_drop=0.0,
):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
flow.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
) # 2*Wh-1 * 2*Ww-1, nH
# get pair-wise relative position index for each token inside the window
coords_h = flow.arange(self.window_size[0])
coords_w = flow.arange(self.window_size[1])
coords = flow.stack(flow.meshgrid(*[coords_h, coords_w])) # 2, Wh, Ww
coords_flatten = flow.flatten(coords, 1) # 2, Wh*Ww
relative_coords = (
coords_flatten[:, :, None] - coords_flatten[:, None, :]
) # 2, Wh*Ww, Wh*Ww
relative_coords = relative_coords.permute(1, 2, 0) # Wh*Ww, Wh*Ww, 2
relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
relative_coords[:, :, 1] += self.window_size[1] - 1
relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
self.register_buffer("relative_position_index", relative_position_index)
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
self.proj = nn.Linear(dim, dim)
self.proj_drop = nn.Dropout(proj_drop)
trunc_normal_(self.relative_position_bias_table, std=0.02)
self.softmax = nn.Softmax(dim=-1)
def forward(self, x, mask=None):
"""
Args:
x: input features with shape of (num_windows*B, N, C)
mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
"""
B_, N, C = x.shape
qkv = (
self.qkv(x)
.reshape(B_, N, 3, self.num_heads, C // self.num_heads)
.permute(2, 0, 3, 1, 4)
)
q, k, v = qkv[0], qkv[1], qkv[2]
q = q * self.scale
attn = flow.matmul(q, k.transpose(-2, -1))
relative_position_bias = self.relative_position_bias_table[
self.relative_position_index.view(-1)
].view(
self.window_size[0] * self.window_size[1],
self.window_size[0] * self.window_size[1],
-1,
) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(
2, 0, 1
) # nH, Wh*Ww, Wh*Ww
attn = attn + relative_position_bias.unsqueeze(0)
if mask is not None:
nW = mask.shape[0]
attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
1
).unsqueeze(0)
attn = attn.view(-1, self.num_heads, N, N)
attn = self.softmax(attn)
else:
attn = self.softmax(attn)
attn = self.attn_drop(attn)
x = flow.matmul(attn, v).transpose(1, 2).reshape(B_, N, C)
x = self.proj(x)
x = self.proj_drop(x)
return x
class SwinTransformerBlock(nn.Module):
r""" Swin Transformer Block.
Args:
dim (int): Number of input channels
input_resolution (tuple[int]): Input resulotion
num_heads (int): Number of attention heads
window_size (int): Window size
shift_size (int): Shift size for SW-MSA
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: ``True``
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
drop (float, optional): Dropout rate. Default: ``0.0``
attn_drop (float, optional): Attention dropout rate. Default: ``0.0``
drop_path (float, optional): Stochastic depth rate. Default: ``0.0``
act_layer (nn.Module, optional): Activation layer. Default: ``nn.GELU``
norm_layer (nn.Module, optional): Normalization layer. Default: ``nn.LayerNorm``
"""
def __init__(
self,
dim,
input_resolution,
num_heads,
window_size=7,
shift_size=0,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
act_layer=nn.GELU,
norm_layer=nn.LayerNorm,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.num_heads = num_heads
self.window_size = window_size
self.shift_size = shift_size
self.mlp_ratio = mlp_ratio
if min(self.input_resolution) <= self.window_size:
# if window size is larger than input resolution, we don't partition windows
self.shift_size = 0
self.window_size = min(self.input_resolution)
assert (
0 <= self.shift_size < self.window_size
), "shift_size must in 0-window_size"
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim,
window_size=to_2tuple(self.window_size),
num_heads=num_heads,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
attn_drop=attn_drop,
proj_drop=drop,
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = Mlp(
in_features=dim,
hidden_features=mlp_hidden_dim,
act_layer=act_layer,
drop=drop,
)
if self.shift_size > 0:
# calculate attention mask for SW-MSA
H, W = self.input_resolution
img_mask = flow.zeros((1, H, W, 1)) # 1 H W 1
h_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
w_slices = (
slice(0, -self.window_size),
slice(-self.window_size, -self.shift_size),
slice(-self.shift_size, None),
)
cnt = 0
for h in h_slices:
for w in w_slices:
img_mask[:, h, w, :] = cnt
cnt += 1
mask_windows = window_partition(
img_mask, self.window_size
) # nW, window_size, window_size, 1
mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
attn_mask = attn_mask.masked_fill(
attn_mask != 0, float(-100.0)
).masked_fill(attn_mask == 0, float(0.0))
else:
attn_mask = None
self.register_buffer("attn_mask", attn_mask)
def forward(self, x):
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
shortcut = x
x = self.norm1(x)
x = x.view(B, H, W, C)
# cyclic shift
if self.shift_size > 0:
shifted_x = flow.roll(
x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
)
else:
shifted_x = x
# partition windows
x_windows = window_partition(
shifted_x, self.window_size
) # nW*B, window_size, window_size, C
x_windows = x_windows.view(
-1, self.window_size * self.window_size, C
) # nW*B, window_size*window_size, C
# W-MSA/SW-MSA
attn_windows = self.attn(
x_windows, mask=self.attn_mask
) # nW*B, window_size*window_size, C
# merge windows
attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
# reverse cyclic shift
if self.shift_size > 0:
x = flow.roll(
shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
)
else:
x = shifted_x
x = x.view(B, H * W, C)
# FFN
x = shortcut + self.drop_path(x)
x = x + self.drop_path(self.mlp(self.norm2(x)))
return x
class PatchMerging(nn.Module):
r""" Patch Merging Layer.
Args:
input_resolution (tuple[int]): Resolution of input feature
dim (int): Number of input channels
norm_layer (nn.Module, optional): Normalization layer. Default: ``nn.LayerNorm``
"""
def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
super().__init__()
self.input_resolution = input_resolution
self.dim = dim
self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
self.norm = norm_layer(4 * dim)
def forward(self, x):
"""
x: B, H*W, C
"""
H, W = self.input_resolution
B, L, C = x.shape
assert L == H * W, "input feature has wrong size"
assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
x = x.view(B, H, W, C)
x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
x = flow.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
x = self.norm(x)
x = self.reduction(x)
return x
class PatchEmbed(nn.Module):
r""" Image to Patch Embedding
Args:
img_size (int): Image size. Default: ``224``
patch_size (int): Patch token size. Default: ``4``
in_chans (int): Number of input image channels. Default: ``3``
embed_dim (int): Number of linear projection output channels. Default: ``96``
norm_layer (nn.Module, optional): Normalization layer. Default: ``None``
"""
def __init__(
self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
):
super().__init__()
img_size = to_2tuple(img_size)
patch_size = to_2tuple(patch_size)
patches_resolution = [
img_size[0] // patch_size[0],
img_size[1] // patch_size[1],
]
self.img_size = img_size
self.patch_size = patch_size
self.patches_resolution = patches_resolution
self.num_patches = patches_resolution[0] * patches_resolution[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
self.proj = nn.Conv2d(
in_chans, embed_dim, kernel_size=patch_size, stride=patch_size
)
if norm_layer is not None:
self.norm = norm_layer(embed_dim)
else:
self.norm = None
def forward(self, x):
B, C, H, W = x.shape
# FIXME look at relaxing size constraints
assert (
H == self.img_size[0] and W == self.img_size[1]
), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
x = self.proj(x).flatten(2).transpose(1, 2) # B Ph*Pw C
if self.norm is not None:
x = self.norm(x)
return x
class BasicLayer(nn.Module):
""" A basic Swin Transformer layer for one stage.
Args:
dim (int): Number of input channels
input_resolution (tuple[int]): Input resolution
depth (int): Number of blocks
num_heads (int): Number of attention heads
window_size (int): Local window size
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: ``True``
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
drop (float, optional): Dropout rate. Default: ``0.0``
attn_drop (float, optional): Attention dropout rate. Default: ``0.0``
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: ``0.0``
norm_layer (nn.Module, optional): Normalization layer. Default: ``nn.LayerNorm``
downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: ``None``
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: ``False``
"""
def __init__(
self,
dim,
input_resolution,
depth,
num_heads,
window_size,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop=0.0,
attn_drop=0.0,
drop_path=0.0,
norm_layer=nn.LayerNorm,
downsample=None,
use_checkpoint=False,
):
super().__init__()
self.dim = dim
self.input_resolution = input_resolution
self.depth = depth
self.use_checkpoint = use_checkpoint
# build blocks
self.blocks = nn.ModuleList(
[
SwinTransformerBlock(
dim=dim,
input_resolution=input_resolution,
num_heads=num_heads,
window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop,
attn_drop=attn_drop,
drop_path=drop_path[i]
if isinstance(drop_path, list)
else drop_path,
norm_layer=norm_layer,
)
for i in range(depth)
]
)
# patch merging layer
if downsample is not None:
self.downsample = downsample(
input_resolution, dim=dim, norm_layer=norm_layer
)
else:
self.downsample = None
def forward(self, x):
for blk in self.blocks:
if self.use_checkpoint:
raise Exception("Torch use Checkpoint!")
# x = checkpoint.checkpoint(blk, x)
else:
x = blk(x)
if self.downsample is not None:
x = self.downsample(x)
return x
class SwinTransformer(nn.Module):
r""" Swin Transformer
A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
https://arxiv.org/pdf/2103.14030
Args:
img_size (int | tuple(int)): Input image size. Default ``224``
patch_size (int | tuple(int)): Patch size. Default: ``4``
in_chans (int): Number of input image channels. Default: ``3``
num_classes (int): Number of classes for classification head. Default: ``1000``
embed_dim (int): Patch embedding dimension. Default: ``96``
depths (tuple(int)): Depth of each Swin Transformer layer
num_heads (tuple(int)): Number of attention heads in different layers
window_size (int): Window size. Default: ``7``
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: ``4``
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: ``True``
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: ``None``
drop_rate (float): Dropout rate. Default: ``0``
attn_drop_rate (float): Attention dropout rate. Default: ``0``
drop_path_rate (float): Stochastic depth rate. Default: ``0.1``
norm_layer (nn.Module): Normalization layer. Default: ``nn.LayerNorm``
ape (bool): If True, add absolute position embedding to the patch embedding. Default: ``False``
patch_norm (bool): If True, add normalization after patch embedding. Default: ``True``
use_checkpoint (bool): Whether to use checkpointing to save memory. Default: ``False``
"""
def __init__(
self,
img_size=224,
patch_size=4,
in_chans=3,
num_classes=1000,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.1,
norm_layer=nn.LayerNorm,
ape=False,
patch_norm=True,
use_checkpoint=False,
**kwargs,
):
super().__init__()
self.num_classes = num_classes
self.num_layers = len(depths)
self.embed_dim = embed_dim
self.ape = ape
self.patch_norm = patch_norm
self.num_features = int(embed_dim * 2 ** (self.num_layers - 1))
self.mlp_ratio = mlp_ratio
# split image into non-overlapping patches
self.patch_embed = PatchEmbed(
img_size=img_size,
patch_size=patch_size,
in_chans=in_chans,
embed_dim=embed_dim,
norm_layer=norm_layer if self.patch_norm else None,
)
num_patches = self.patch_embed.num_patches
patches_resolution = self.patch_embed.patches_resolution
self.patches_resolution = patches_resolution
# absolute position embedding
if self.ape:
self.absolute_pos_embed = nn.Parameter(
flow.zeros(1, num_patches, embed_dim)
)
trunc_normal_(self.absolute_pos_embed, std=0.02)
self.pos_drop = nn.Dropout(p=drop_rate)
# stochastic depth
dpr = [
x.item() for x in flow.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
# build layers
self.layers = nn.ModuleList()
for i_layer in range(self.num_layers):
layer = BasicLayer(
dim=int(embed_dim * 2 ** i_layer),
input_resolution=(
patches_resolution[0] // (2 ** i_layer),
patches_resolution[1] // (2 ** i_layer),
),
depth=depths[i_layer],
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias,
qk_scale=qk_scale,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
norm_layer=norm_layer,
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
use_checkpoint=use_checkpoint,
)
self.layers.append(layer)
self.norm = norm_layer(self.num_features)
self.avgpool = nn.AdaptiveAvgPool1d(1)
self.head = (
nn.Linear(self.num_features, num_classes)
if num_classes > 0
else nn.Identity()
)
self.apply(self._init_weights)
def _init_weights(self, m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
def forward_features(self, x):
x = self.patch_embed(x)
if self.ape:
x = x + self.absolute_pos_embed
x = self.pos_drop(x)
for layer in self.layers:
x = layer(x)
x = self.norm(x) # B L C
x = self.avgpool(x.transpose(1, 2)) # B C 1
x = flow.flatten(x, 1)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def _create_swin_transformer(arch, pretrained=False, progress=True, **model_kwargs):
model = SwinTransformer(**model_kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
model.load_state_dict(state_dict)
return model
[docs]@ModelCreator.register_model
def swin_tiny_patch4_window7_224(pretrained=False, progress=True, **kwargs):
"""
Constructs Swin-T 224x224 model trained on ImageNet-1k.
.. note::
Swin-T 224x224 model from `"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> swin_tiny_patch4_window7_224 = flowvision.models.swin_tiny_patch4_window7_224(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=224,
patch_size=4,
window_size=7,
embed_dim=96,
depths=(2, 2, 6, 2),
num_heads=(3, 6, 12, 24),
drop_path_rate=0.2,
**kwargs,
)
return _create_swin_transformer(
"swin_tiny_patch4_window7_224",
pretrained=pretrained,
progress=progress,
**model_kwargs,
)
[docs]@ModelCreator.register_model
def swin_small_patch4_window7_224(pretrained=False, progress=True, **kwargs):
"""
Constructs Swin-S 224x224 model trained on ImageNet-1k.
.. note::
Swin-S 224x224 model from `"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> swin_small_patch4_window7_224 = flowvision.models.swin_small_patch4_window7_224(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=224,
patch_size=4,
window_size=7,
embed_dim=96,
depths=(2, 2, 18, 2),
num_heads=(3, 6, 12, 24),
drop_path_rate=0.3,
**kwargs,
)
return _create_swin_transformer(
"swin_small_patch4_window7_224",
pretrained=pretrained,
progress=progress,
**model_kwargs,
)
[docs]@ModelCreator.register_model
def swin_base_patch4_window7_224(pretrained=False, progress=True, **kwargs):
"""
Constructs Swin-B 224x224 model trained on ImageNet-1k.
.. note::
Swin-B 224x224 model from `"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> swin_base_patch4_window7_224 = flowvision.models.swin_base_patch4_window7_224(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=224,
patch_size=4,
window_size=7,
embed_dim=128,
depths=(2, 2, 18, 2),
num_heads=(4, 8, 16, 32),
drop_path_rate=0.5,
**kwargs,
)
return _create_swin_transformer(
"swin_base_patch4_window7_224",
pretrained=pretrained,
progress=progress,
**model_kwargs,
)
[docs]@ModelCreator.register_model
def swin_base_patch4_window12_384(pretrained=False, progress=True, **kwargs):
"""
Constructs Swin-B 384x384 model trained on ImageNet-1k.
.. note::
Swin-B 384x384 model from `"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> swin_base_patch4_window12_384 = flowvision.models.swin_base_patch4_window12_384(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=384,
patch_size=4,
window_size=12,
embed_dim=128,
depths=(2, 2, 18, 2),
num_heads=(4, 8, 16, 32),
drop_path_rate=0.5,
**kwargs,
)
return _create_swin_transformer(
"swin_base_patch4_window12_384",
pretrained=pretrained,
progress=progress,
**model_kwargs,
)
[docs]@ModelCreator.register_model
def swin_base_patch4_window7_224_in22k_to_1k(pretrained=False, progress=True, **kwargs):
"""
Constructs Swin-B 224x224 model pretrained on ImageNet-22k and fine tuned on ImageNet-1k.
.. note::
Swin-B 224x224 model from `"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> swin_base_patch4_window7_224_in22k_to_1k = flowvision.models.swin_base_patch4_window7_224_in22k_to_1k(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=224,
patch_size=4,
window_size=7,
embed_dim=128,
depths=(2, 2, 18, 2),
num_heads=(4, 8, 16, 32),
drop_path_rate=0.5,
**kwargs,
)
return _create_swin_transformer(
"swin_base_patch4_window7_224_in22k_to_1k",
pretrained=pretrained,
progress=progress,
**model_kwargs,
)
[docs]@ModelCreator.register_model
def swin_base_patch4_window12_384_in22k_to_1k(
pretrained=False, progress=True, **kwargs
):
"""
Constructs Swin-B 384x384 model pretrained on ImageNet-22k and fine tuned on ImageNet-1k.
.. note::
Swin-B 384x384 model from `"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> swin_base_patch4_window12_384_in22k_to_1k = flowvision.models.swin_base_patch4_window12_384_in22k_to_1k(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=384,
patch_size=4,
window_size=12,
embed_dim=128,
depths=(2, 2, 18, 2),
num_heads=(4, 8, 16, 32),
drop_path_rate=0.5,
**kwargs,
)
return _create_swin_transformer(
"swin_base_patch4_window12_384_in22k_to_1k",
pretrained=pretrained,
progress=progress,
**model_kwargs,
)
[docs]@ModelCreator.register_model
def swin_large_patch4_window7_224_in22k_to_1k(
pretrained=False, progress=True, **kwargs
):
"""
Constructs Swin-L 224x224 model pretrained on ImageNet-22k and fine tuned on ImageNet-1k.
.. note::
Swin-L 224x224 model from `"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> swin_large_patch4_window7_224_in22k_to_1k = flowvision.models.swin_large_patch4_window7_224_in22k_to_1k(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=224,
patch_size=4,
window_size=7,
embed_dim=192,
depths=(2, 2, 18, 2),
num_heads=(6, 12, 24, 48),
drop_path_rate=0.5,
**kwargs,
)
return _create_swin_transformer(
"swin_large_patch4_window7_224_in22k_to_1k",
pretrained=pretrained,
progress=progress,
**model_kwargs,
)
[docs]@ModelCreator.register_model
def swin_large_patch4_window12_384_in22k_to_1k(
pretrained=False, progress=True, **kwargs
):
"""
Constructs Swin-L 384x384 model pretrained on ImageNet-22k and fine tuned on ImageNet-1k.
.. note::
Swin-L 384x384 model from `"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" <https://arxiv.org/pdf/2103.14030>`_.
Args:
pretrained (bool): Whether to download the pre-trained model on ImageNet. Default: ``False``
progress (bool): If True, displays a progress bar of the download to stderr. Default: ``True``
For example:
.. code-block:: python
>>> import flowvision
>>> swin_large_patch4_window12_384_in22k_to_1k = flowvision.models.swin_large_patch4_window12_384_in22k_to_1k(pretrained=False, progress=True)
"""
model_kwargs = dict(
img_size=384,
patch_size=4,
window_size=12,
embed_dim=192,
depths=(2, 2, 18, 2),
num_heads=(6, 12, 24, 48),
drop_path_rate=0.5,
**kwargs,
)
return _create_swin_transformer(
"swin_large_patch4_window12_384_in22k_to_1k",
pretrained=pretrained,
progress=progress,
**model_kwargs,
)